GPU-Accelerated Machine Learning Inference as a Service for Computing in Neutrino Experiments.
Michael Wang,Tingjun Yang,Maria Acosta Flechas,Philip Harris,Benjamin Hawks,Burt Holzman,K. Knoepfel,Jeffrey Krupa,Kevin Pedro,Nhan Tran,Nhan Tran +10 more
- Vol. 3, pp 604083-604083
TLDR
In this article, the authors explore a computing model in which heterogeneous computing with GPU coprocessors is made available as a web service, which can be efficiently and elastically deployed to provide the right amount of computing for a given processing task.Abstract:
Machine learning algorithms are becoming increasingly prevalent and performant in the reconstruction of events in accelerator-based neutrino experiments. These sophisticated algorithms can be computationally expensive. At the same time, the data volumes of such experiments are rapidly increasing. The demand to process billions of neutrino events with many machine learning algorithm inferences creates a computing challenge. We explore a computing model in which heterogeneous computing with GPU coprocessors is made available as a web service. The coprocessors can be efficiently and elastically deployed to provide the right amount of computing for a given processing task. With our approach, Services for Optimized Network Inference on Coprocessors (SONIC), we integrate GPU acceleration specifically for the ProtoDUNE-SP reconstruction chain without disrupting the native computing workflow. With our integrated framework, we accelerate the most time-consuming task, track and particle shower hit identification, by a factor of 17. This results in a factor of 2.7 reduction in the total processing time when compared with CPU-only production. For this particular task, only 1 GPU is required for every 68 CPU threads, providing a cost-effective solution.read more
Citations
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Journal ArticleDOI
GPU coprocessors as a service for deep learning inference in high energy physics
Jeffrey Krupa,Kelvin Lin,Maria Acosta Flechas,Jack Dinsmore,Javier Duarte,Philip Harris,Scott Hauck,Burt Holzman,Shih-Chieh Hsu,Thomas Klijnsma,Mia Liu,Kevin Pedro,Dylan Rankin,Natchanon Suaysom,Matthew Trahms,Nhan Tran,Nhan Tran +16 more
TL;DR: A comprehensive exploration of the use of GPU-based hardware acceleration for deep learning inference within the data reconstruction workflow of high energy physics is presented.
Journal ArticleDOI
GPU coprocessors as a service for deep learning inference in high energy physics
Jeffrey Krupa,Kelvin Lin,Maria Acosta Flechas,Jack Dinsmore,Javier Duarte,Philip Harris,Scott Hauck,Burt Holzman,Shih-Chieh Hsu,Thomas Klijnsma,Mia Liu,Kevin Pedro,Dylan Rankin,Natchanon Suaysom,Matthew Trahms,Nhan Tran,Nhan Tran +16 more
TL;DR: In this article, the authors explore the use of GPU-based hardware acceleration for deep learning inference within the data reconstruction workflow of high energy physics at the CERN Large Hadron Collider (LHC).
Charged Particle Tracking via Edge-Classifying Interaction Networks
Gage Dezoort,Savannah Thais,Javier Duarte,Vesal Razavimaleki,Markus Atkinson,Isobel Ojalvo,Mark Neubauer,Peter Elmer +7 more
TL;DR: In this article, the authors adapt the physics-motivated interaction network (IN) GNN to the problem of particle tracking in pileup conditions similar to those expected at the high-luminosity Large Hadron Collider.
Journal ArticleDOI
Machine learning in the search for new fundamental physics
G. Karagiorgi,Bowen Wang,Anna Bondarenko,Nabeel Taha Ali Belal , Khalid Akbar Abdullah,Sayed Khalil Kohi +4 more
TL;DR: A review of the state-of-the-art methods and applications for new physics searches in the context of terrestrial high-energy physics experiments, including the Large Hadron Collider, rare event searches and neutrino experiments, can be found in this paper .
Posted Content
Applications and Techniques for Fast Machine Learning in Science
Allison McCarn Deiana,Nhan Tran,Joshua Agar,Michaela Blott,Giuseppe Di Guglielmo,Javier Duarte,Philip Harris,Scott Hauck,Mia Liu,Mark Neubauer,Jennifer Ngadiuba,Seda Ogrenci-Memik,Maurizio Pierini,Thea Klaeboe Aarrestad,Steffen Bahr,Jurgen Becker,Anne-Sophie Berthold,Richard J. Bonventre,Tomas E. Muller Bravo,Markus Diefenthaler,Zhen Dong,Nick Fritzsche,Amir Gholami,Ekaterina Govorkova,Kyle J Hazelwood,Christian Herwig,Babar Khan,Sehoon Kim,Thomas Klijnsma,Yaling Liu,Kin Ho Lo,Tri Minh Nguyen,Gianantonio Pezzullo,Seyedramin Rasoulinezhad,Ryan A. Rivera,Kate Scholberg,Justin Selig,Sougata Sen,Dmitri Strukov,William Tang,Savannah Thais,Kai Lukas Unger,Ricardo Vilalta,Belinavon Krosigk,Thomas K. Warburton,Maria Acosta Flechas,Anthony Aportela,Thomas Calvet,Leonardo Cristella,Daniel Diaz,Caterina Doglioni,Maria Domenica Galati,Elham E Khoda,Farah Fahim,Davide Giri,Benjamin Hawks,Duc Hoang,Burt Holzman,Shih-Chieh Hsu,Sergo Jindariani,Iris Johnson,Raghav Kansal,Ryan Kastner,Erik Katsavounidis,Jeffrey Krupa,Pan Li,Sandeep Madireddy,Ethan Marx,Patrick McCormack,Andres Meza,Jovan Mitrevski,Mohammed Attia Mohammed,Farouk Mokhtar,Eric Moreno,Srishti Nagu,Rohin Narayan,Noah Palladino,Zhiqiang Que,Sang Eon Park,Subramanian Ramamoorthy,Dylan Rankin,Simon Rothman,Ashish Sharma,Sioni Summers,Pietro Vischia,Jean-Roch Vlimant,Olivia Weng +86 more
TL;DR: In this article, the authors discuss applications and techniques for fast machine learning (ML) in science, the concept of integrating power ML methods into the real-time experimental data processing loop to accelerate scientific discovery.
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TL;DR: MicroBooNE as discussed by the authors is the first phase of the Short Baseline Neutrino program, located at Fermilab, and will utilize the capabilities of liquid argon detectors to examine a rich assortment of physics topics.
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Supernova Neutrino Detection
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